----------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/jordimunoz/Dropbox/immigration and welfare/PSRM/Analysis/Replication/replication_munoz_pardos_PSRM.log
  log type:  text
 opened on:   4 Apr 2017, 01:16:19

. use "replication_munoz_pardos_PSRM.dta", clear

. 
. *------------------------------------------------------------*
. *----------Data preparation----------------------------------*
. *------------------------------------------------------------*
. 
. *Generate treatment variables
. gen treatment=.
(3.617 missing values generated)

. replace treatment=1 if SJ1a_Universal_ImigrantsMentione!=9
(897 real changes made)

. replace treatment=2 if SJ1b_Universal_ImigrantsNotMenti!=9
(892 real changes made)

. replace treatment=3 if SJ1c_MeansTested_ImigrantsMentio!=9
(930 real changes made)

. replace treatment=4 if SJ1d_MeansTested_ImigrantsNotMen!=9
(900 real changes made)

. label define treatment 1"Univ Immi" 2"Universal NI" 3"MT Immi" 4"MT NI"

. label values treatment treatment

. tab treat

   treatment |      Freq.     Percent        Cum.
-------------+-----------------------------------
   Univ Immi |        896       24,77       24,77
Universal NI |        891       24,63       49,41
     MT Immi |        930       25,71       75,12
       MT NI |        900       24,88      100,00
-------------+-----------------------------------
       Total |       3617      100,00

. 
. gen universal=0

. replace universal=1 if treatment<3
(1.787 real changes made)

. label define universal 0"Means-Tested" 1"Universal"

. label values universal universal

. tab universal

   universal |      Freq.     Percent        Cum.
-------------+-----------------------------------
Means-Tested |       1830       50,59       50,59
   Universal |       1787       49,41      100,00
-------------+-----------------------------------
       Total |       3617      100,00

. 
. gen immi=0

. replace immi=1 if treatment==1
(896 real changes made)

. replace immi=1 if treatment==3
(930 real changes made)

. label define immi 0"No immigration priming" 1"Immigration priming"

. label values immi immi

. tab immi

                  immi |      Freq.     Percent        Cum.
-----------------------+-----------------------------------
No immigration priming |       1791       49,52       49,52
   Immigration priming |       1826       50,48      100,00
-----------------------+-----------------------------------
                 Total |       3617      100,00

. 
. *Generate dependent variable
. gen dv= SJ1b_Universal_ImigrantsNotMenti

. replace dv= SJ1c_MeansTested_ImigrantsMentio if dv==9
(929 real changes made)

. replace dv= SJ1d_MeansTested_ImigrantsNotMen if dv==9
(900 real changes made)

. replace dv= SJ1a_Universal_ImigrantsMentione if dv==9
(896 real changes made)

. recode dv (5=.a)
(dv: 359 changes made)

. label values dv SJ1d_MeansTested_ImigrantsNotMen

. tab dv

                                     dv |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
These benefits should be removed and th |        737       22,62       22,62
These benefits should be reduced, and t |        892       27,38       50,00
    These benefits should be maintained |       1498       45,98       95,98
These benefits should be expanded, usin |        131        4,02      100,00
----------------------------------------+-----------------------------------
                                  Total |       3258      100,00

. 
. *Generate moderators
. gen culture= SJ4 if SJ4<12
(157 missing values generated)

. replace culture=culture-1
(3.460 real changes made)

. gen prejudice=-culture+10 /*from less to more prejudice*/
(157 missing values generated)

. 
. gen deserving1=SJ5a-1 if SJ5a<12
(216 missing values generated)

. tab deserving1

 deserving1 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        210        6,17        6,17
          1 |         73        2,15        8,32
          2 |        114        3,35       11,67
          3 |        124        3,65       15,32
          4 |        113        3,32       18,64
          5 |        813       23,90       42,55
          6 |        259        7,62       50,16
          7 |        368       10,82       60,98
          8 |        409       12,03       73,01
          9 |        203        5,97       78,98
         10 |        715       21,02      100,00
------------+-----------------------------------
      Total |       3401      100,00

. 
end of do-file

. do "/var/folders/ct/q1ww4xg51b7_4qynkp39sn100000gn/T//SD11644.000000"

. *Table 3. Results of the experiment (column percentages).
. tab dv treat, col nofreq

                      |                  treatment
                   dv | Univ Immi  Universal    MT Immi      MT NI |     Total
----------------------+--------------------------------------------+----------
These benefits should |     29,91      18,26      29,70      12,58 |     22,62 
These benefits should |     33,04      27,58      29,10      19,90 |     27,38 
These benefits should |     34,67      51,30      37,37      60,56 |     45,98 
These benefits should |      2,38       2,86       3,83       6,96 |      4,02 
----------------------+--------------------------------------------+----------
                Total |    100,00     100,00     100,00     100,00 |    100,00 


. tab dv immi, col nofreq

                      |         immi
                   dv | No immigr  Immigrati |     Total
----------------------+----------------------+----------
These benefits should |     15,39      29,80 |     22,62 
These benefits should |     23,71      31,03 |     27,38 
These benefits should |     55,97      36,05 |     45,98 
These benefits should |      4,93       3,12 |      4,02 
----------------------+----------------------+----------
                Total |    100,00     100,00 |    100,00 


. 
. *Proportion tests (table 3)
. tab dv, gen(dv)

                                     dv |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
These benefits should be removed and th |        737       22,62       22,62
These benefits should be reduced, and t |        892       27,38       50,00
    These benefits should be maintained |       1498       45,98       95,98
These benefits should be expanded, usin |        131        4,02      100,00
----------------------------------------+-----------------------------------
                                  Total |       3258      100,00

. 
. foreach var of varlist dv1-dv4 {
  2. prtest `var', by(immi)
  3. }

Two-sample test of proportions          No immigrati: Number of obs =     1624
                                         Immigration: Number of obs =     1634
------------------------------------------------------------------------------
    Variable |       Mean   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
No immigrati |   ,1539409   ,0089554                      ,1363887    ,1714931
 Immigration |   ,2980416   ,0113154                      ,2758639    ,3202193
-------------+----------------------------------------------------------------
        diff |  -,1441007   ,0144304                     -,1723838   -,1158177
             |  under Ho:   ,0146597    -9,83   0,000
------------------------------------------------------------------------------
        diff = prop(No immigrati) - prop(Immigration)             z =  -9,8297
    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = 0,0000         Pr(|Z| > |z|) = 0,0000          Pr(Z > z) = 1,0000

Two-sample test of proportions          No immigrati: Number of obs =     1624
                                         Immigration: Number of obs =     1634
------------------------------------------------------------------------------
    Variable |       Mean   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
No immigrati |    ,237069   ,0105533                      ,2163849     ,257753
 Immigration |   ,3102815   ,0114443                      ,2878512    ,3327119
-------------+----------------------------------------------------------------
        diff |  -,0732126   ,0155674                      -,103724   -,0427011
             |  under Ho:   ,0156241    -4,69   0,000
------------------------------------------------------------------------------
        diff = prop(No immigrati) - prop(Immigration)             z =  -4,6859
    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = 0,0000         Pr(|Z| > |z|) = 0,0000          Pr(Z > z) = 1,0000

Two-sample test of proportions          No immigrati: Number of obs =     1624
                                         Immigration: Number of obs =     1634
------------------------------------------------------------------------------
    Variable |       Mean   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
No immigrati |   ,5597291   ,0123184                      ,5355854    ,5838728
 Immigration |   ,3604651   ,0118778                       ,337185    ,3837453
-------------+----------------------------------------------------------------
        diff |   ,1992639   ,0171122                      ,1657247    ,2328032
             |  under Ho:    ,017463    11,41   0,000
------------------------------------------------------------------------------
        diff = prop(No immigrati) - prop(Immigration)             z =  11,4107
    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = 1,0000         Pr(|Z| > |z|) = 0,0000          Pr(Z > z) = 0,0000

Two-sample test of proportions          No immigrati: Number of obs =     1624
                                         Immigration: Number of obs =     1634
------------------------------------------------------------------------------
    Variable |       Mean   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
No immigrati |   ,0492611   ,0053702                      ,0387357    ,0597865
 Immigration |   ,0312118   ,0043018                      ,0227804    ,0396431
-------------+----------------------------------------------------------------
        diff |   ,0180493   ,0068807                      ,0045634    ,0315353
             |  under Ho:   ,0068834     2,62   0,009
------------------------------------------------------------------------------
        diff = prop(No immigrati) - prop(Immigration)             z =   2,6221
    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = 0,9956         Pr(|Z| > |z|) = 0,0087          Pr(Z > z) = 0,0044

. 
. foreach var of varlist dv1-dv4 {
  2. prtest `var' if univ==1, by(immi)
  3. }

Two-sample test of proportions          No immigrati: Number of obs =      805
                                         Immigration: Number of obs =      799
------------------------------------------------------------------------------
    Variable |       Mean   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
No immigrati |   ,1826087   ,0136169                      ,1559201    ,2092973
 Immigration |   ,2991239   ,0161984                      ,2673756    ,3308722
-------------+----------------------------------------------------------------
        diff |  -,1165152   ,0211615                      -,157991   -,0750395
             |  under Ho:   ,0213473    -5,46   0,000
------------------------------------------------------------------------------
        diff = prop(No immigrati) - prop(Immigration)             z =  -5,4581
    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = 0,0000         Pr(|Z| > |z|) = 0,0000          Pr(Z > z) = 1,0000

Two-sample test of proportions          No immigrati: Number of obs =      805
                                         Immigration: Number of obs =      799
------------------------------------------------------------------------------
    Variable |       Mean   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
No immigrati |   ,2757764   ,0157513                      ,2449044    ,3066484
 Immigration |    ,330413   ,0166402                      ,2977988    ,3630272
-------------+----------------------------------------------------------------
        diff |  -,0546366   ,0229129                     -,0995451   -,0097282
             |  under Ho:   ,0229491    -2,38   0,017
------------------------------------------------------------------------------
        diff = prop(No immigrati) - prop(Immigration)             z =  -2,3808
    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = 0,0086         Pr(|Z| > |z|) = 0,0173          Pr(Z > z) = 0,9914

Two-sample test of proportions          No immigrati: Number of obs =      805
                                         Immigration: Number of obs =      799
------------------------------------------------------------------------------
    Variable |       Mean   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
No immigrati |   ,5130435   ,0176167                      ,4785154    ,5475716
 Immigration |   ,3466834   ,0168366                      ,3136842    ,3796825
-------------+----------------------------------------------------------------
        diff |   ,1663601   ,0243684                      ,1185989    ,2141213
             |  under Ho:   ,0247243     6,73   0,000
------------------------------------------------------------------------------
        diff = prop(No immigrati) - prop(Immigration)             z =   6,7286
    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = 1,0000         Pr(|Z| > |z|) = 0,0000          Pr(Z > z) = 0,0000

Two-sample test of proportions          No immigrati: Number of obs =      805
                                         Immigration: Number of obs =      799
------------------------------------------------------------------------------
    Variable |       Mean   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
No immigrati |   ,0285714   ,0058718                      ,0170629      ,04008
 Immigration |   ,0237797   ,0053902                      ,0132151    ,0343443
-------------+----------------------------------------------------------------
        diff |   ,0047917   ,0079707                     -,0108306     ,020414
             |  under Ho:   ,0079743     0,60   0,548
------------------------------------------------------------------------------
        diff = prop(No immigrati) - prop(Immigration)             z =   0,6009
    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = 0,7260         Pr(|Z| > |z|) = 0,5479          Pr(Z > z) = 0,2740

. 
. foreach var of varlist dv1-dv4 {
  2. prtest `var' if univ==0, by(immi)
  3. }

Two-sample test of proportions          No immigrati: Number of obs =      819
                                         Immigration: Number of obs =      835
------------------------------------------------------------------------------
    Variable |       Mean   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
No immigrati |   ,1257631   ,0115864                      ,1030542    ,1484721
 Immigration |    ,297006    ,015813                       ,266013     ,327999
-------------+----------------------------------------------------------------
        diff |  -,1712429   ,0196035                      -,209665   -,1328207
             |  under Ho:   ,0201082    -8,52   0,000
------------------------------------------------------------------------------
        diff = prop(No immigrati) - prop(Immigration)             z =  -8,5161
    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = 0,0000         Pr(|Z| > |z|) = 0,0000          Pr(Z > z) = 1,0000

Two-sample test of proportions          No immigrati: Number of obs =      819
                                         Immigration: Number of obs =      835
------------------------------------------------------------------------------
    Variable |       Mean   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
No immigrati |   ,1990232   ,0139515                      ,1716788    ,2263676
 Immigration |    ,291018   ,0157193                      ,2602086    ,3218273
-------------+----------------------------------------------------------------
        diff |  -,0919948   ,0210176                     -,1331886    -,050801
             |  under Ho:    ,021165    -4,35   0,000
------------------------------------------------------------------------------
        diff = prop(No immigrati) - prop(Immigration)             z =  -4,3466
    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = 0,0000         Pr(|Z| > |z|) = 0,0000          Pr(Z > z) = 1,0000

Two-sample test of proportions          No immigrati: Number of obs =      819
                                         Immigration: Number of obs =      835
------------------------------------------------------------------------------
    Variable |       Mean   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
No immigrati |   ,6056166   ,0170772                      ,5721459    ,6390873
 Immigration |   ,3736527   ,0167417                      ,3408397    ,4064657
-------------+----------------------------------------------------------------
        diff |   ,2319639   ,0239147                      ,1850919    ,2788359
             |  under Ho:   ,0245832     9,44   0,000
------------------------------------------------------------------------------
        diff = prop(No immigrati) - prop(Immigration)             z =   9,4359
    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = 1,0000         Pr(|Z| > |z|) = 0,0000          Pr(Z > z) = 0,0000

Two-sample test of proportions          No immigrati: Number of obs =      819
                                         Immigration: Number of obs =      835
------------------------------------------------------------------------------
    Variable |       Mean   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
No immigrati |   ,0695971   ,0088918                      ,0521695    ,0870247
 Immigration |   ,0383234   ,0066436                      ,0253021    ,0513446
-------------+----------------------------------------------------------------
        diff |   ,0312737   ,0110996                      ,0095189    ,0530285
             |  under Ho:   ,0110968     2,82   0,005
------------------------------------------------------------------------------
        diff = prop(No immigrati) - prop(Immigration)             z =   2,8183
    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = 0,9976         Pr(|Z| > |z|) = 0,0048          Pr(Z > z) = 0,0024

. 
. *Figure 1
. reg dv immi##c.prej

      Source |       SS           df       MS      Number of obs   =     3.180
-------------+----------------------------------   F(3, 3176)      =    225,14
       Model |  418,511518         3  139,503839   Prob > F        =    0,0000
    Residual |  1967,93628     3.176  ,619627292   R-squared       =    0,1754
-------------+----------------------------------   Adj R-squared   =    0,1746
       Total |   2386,4478     3.179  ,750691349   Root MSE        =    ,78716

--------------------------------------------------------------------------------------
                  dv |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
                immi |
Immigration priming  |   ,1660825   ,0606431     2,74   0,006     ,0471788    ,2849861
           prejudice |  -,0413688    ,006562    -6,30   0,000     -,054235   -,0285026
                     |
    immi#c.prejudice |
Immigration priming  |  -,0939396   ,0091454   -10,27   0,000    -,1118711   -,0760082
                     |
               _cons |   2,742703   ,0436444    62,84   0,000     2,657129    2,828277
--------------------------------------------------------------------------------------

. margins, dydx(immi) over(prej)

Average marginal effects                        Number of obs     =      3.180
Model VCE    : OLS

Expression   : Linear prediction, predict()
dy/dx w.r.t. : 1.immi
over         : prejudice

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.immi       |
   prejudice |
          0  |   ,1660825   ,0606431     2,74   0,006     ,0471788    ,2849861
          1  |   ,0721428   ,0526933     1,37   0,171    -,0311734    ,1754591
          2  |  -,0217968   ,0451978    -0,48   0,630    -,1104167    ,0668231
          3  |  -,1157365   ,0384238    -3,01   0,003    -,1910744   -,0403986
          4  |  -,2096761   ,0328207    -6,39   0,000    -,2740281   -,1453241
          5  |  -,3036158   ,0290739   -10,44   0,000    -,3606213   -,2466102
          6  |  -,3975554   ,0279403   -14,23   0,000    -,4523382   -,3427726
          7  |   -,491495   ,0297204   -16,54   0,000    -,5497681    -,433222
          8  |  -,5854347    ,033959   -17,24   0,000    -,6520185   -,5188508
          9  |  -,6793743   ,0398799   -17,04   0,000    -,7575673   -,6011813
         10  |   -,773314   ,0468495   -16,51   0,000    -,8651723   -,6814557
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. marginsplot, yline(0) recast(line) recastci(rline) title("Cultural prejudice") xtitle("Cultural prejudice") ytitle("Predic
> ted change in support") name(fig1a)

  Variables that uniquely identify margins: prejudice

. 
. reg dv immi##c.deserving1

      Source |       SS           df       MS      Number of obs   =     3.121
-------------+----------------------------------   F(3, 3117)      =    212,43
       Model |  397,978497         3  132,659499   Prob > F        =    0,0000
    Residual |  1946,49251     3.117  ,624476261   R-squared       =    0,1698
-------------+----------------------------------   Adj R-squared   =    0,1690
       Total |    2344,471     3.120  ,751433014   Root MSE        =    ,79024

--------------------------------------------------------------------------------------
                  dv |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
                immi |
Immigration priming  |  -,0664394   ,0679167    -0,98   0,328    -,1996055    ,0667267
          deserving1 |  -,0768928   ,0069716   -11,03   0,000    -,0905621   -,0632235
                     |
   immi#c.deserving1 |
Immigration priming  |  -,0474381   ,0097067    -4,89   0,000    -,0664702    -,028406
                     |
               _cons |   2,978107   ,0484373    61,48   0,000     2,883135    3,073079
--------------------------------------------------------------------------------------

. margins, dydx(immi) over(deserving1)

Average marginal effects                        Number of obs     =      3.121
Model VCE    : OLS

Expression   : Linear prediction, predict()
dy/dx w.r.t. : 1.immi
over         : deserving1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.immi       |
  deserving1 |
          0  |  -,0664394   ,0679167    -0,98   0,328    -,1996055    ,0667267
          1  |  -,1138775   ,0592307    -1,92   0,055    -,2300126    ,0022577
          2  |  -,1613156   ,0509147    -3,17   0,002    -,2611453   -,0614858
          3  |  -,2087537   ,0431831    -4,83   0,000    -,2934238   -,1240836
          4  |  -,2561918   ,0364099    -7,04   0,000    -,3275817   -,1848019
          5  |  -,3036299   ,0312254    -9,72   0,000    -,3648544   -,2424054
          6  |   -,351068   ,0285098   -12,31   0,000     -,406968    -,295168
          7  |  -,3985061   ,0289661   -13,76   0,000    -,4553006   -,3417116
          8  |  -,4459442   ,0324606   -13,74   0,000    -,5095906   -,3822978
          9  |  -,4933823   ,0381679   -12,93   0,000    -,5682191   -,4185455
         10  |  -,5408204   ,0452584   -11,95   0,000    -,6295598    -,452081
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. marginsplot, yline(0) recast(line) recastci(rline) title("Deservingness Beliefs") xtitle("Deservingness") ytitle("Predicte
> d change in support") name(fig1b)

  Variables that uniquely identify margins: deserving1

. gr combine fig1a fig1b, title("Effect of immigration treatment, by prejudice and deservingness")

. graph save fig1, replace
(file fig1.gph saved)

. graph export fig1.pdf, replace
(file /Users/jordimunoz/Dropbox/immigration and welfare/PSRM/Analysis/Replication/fig1.pdf written in PDF format)

. 
. *Figure 2
. reg dv immi##universal

      Source |       SS           df       MS      Number of obs   =     3.258
-------------+----------------------------------   F(3, 3254)      =     66,29
       Model |  140,413728         3  46,8045759   Prob > F        =    0,0000
    Residual |  2297,36804     3.254  ,706013534   R-squared       =    0,0576
-------------+----------------------------------   Adj R-squared   =    0,0567
       Total |  2437,78177     3.257  ,748474599   Root MSE        =    ,84025

------------------------------------------------------------------------------------------------
                            dv |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------------+----------------------------------------------------------------
                          immi |
          Immigration priming  |  -,4657542   ,0413227   -11,27   0,000    -,5467754    -,384733
                               |
                     universal |
                    Universal  |    -,23147   ,0417023    -5,55   0,000    -,3132353   -,1497046
                               |
                immi#universal |
Immigration priming#Universal  |   ,1732955   ,0588916     2,94   0,003      ,057827    ,2887639
                               |
                         _cons |   2,619048   ,0293606    89,20   0,000     2,561481    2,676615
------------------------------------------------------------------------------------------------

. margins, over(immi universal)

Predictive margins                              Number of obs     =      3.258
Model VCE    : OLS

Expression   : Linear prediction, predict()
over         : immi universal

------------------------------------------------------------------------------------------------------
                                     |            Delta-method
                                     |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------------------+----------------------------------------------------------------
                      immi#universal |
No immigration priming#Means-Tested  |   2,619048   ,0293606    89,20   0,000     2,561481    2,676615
   No immigration priming#Universal  |   2,387578   ,0296148    80,62   0,000     2,329512    2,445643
   Immigration priming#Means-Tested  |   2,153293   ,0290779    74,05   0,000     2,096281    2,210306
      Immigration priming#Universal  |   2,095119   ,0297258    70,48   0,000     2,036836    2,153402
------------------------------------------------------------------------------------------------------

. marginsplot, title("Effect of immigration, by type of policy") ytitle("Predicted support") xtitle("")

  Variables that uniquely identify margins: immi universal

. graph save fig2, replace
(file fig2.gph saved)

. graph export fig2.pdf, replace
(file /Users/jordimunoz/Dropbox/immigration and welfare/PSRM/Analysis/Replication/fig2.pdf written in PDF format)

. 
. *Figure 3a
. reg dv c.deserving1##universal if immi==1

      Source |       SS           df       MS      Number of obs   =     1.572
-------------+----------------------------------   F(3, 1568)      =    113,08
       Model |  216,094822         3  72,0316075   Prob > F        =    0,0000
    Residual |  998,833931     1.568  ,637011435   R-squared       =    0,1779
-------------+----------------------------------   Adj R-squared   =    0,1763
       Total |  1214,92875     1.571  ,773347392   Root MSE        =    ,79813

----------------------------------------------------------------------------------------
                    dv |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
            deserving1 |  -,1400789    ,009677   -14,48   0,000    -,1590601   -,1210976
                       |
             universal |
            Universal  |  -,2538148   ,0961699    -2,64   0,008    -,4424499   -,0651797
                       |
universal#c.deserving1 |
            Universal  |   ,0312734   ,0136432     2,29   0,022     ,0045127    ,0580342
                       |
                 _cons |   3,038951    ,068126    44,61   0,000     2,905324    3,172579
----------------------------------------------------------------------------------------

. est store Deservingness

. margins, dydx(deserving1) at(universal=(0 1))

Average marginal effects                        Number of obs     =      1.572
Model VCE    : OLS

Expression   : Linear prediction, predict()
dy/dx w.r.t. : deserving1

1._at        : universal       =           0

2._at        : universal       =           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
deserving1   |
         _at |
          1  |  -,1400789    ,009677   -14,48   0,000    -,1590601   -,1210976
          2  |  -,1088054   ,0096173   -11,31   0,000    -,1276695   -,0899414
------------------------------------------------------------------------------

. marginsplot, recast(scatter) recastci(rspike) title("Effects of deservingness beliefs, under immigration treatment") ///
>         subtitle("Average Marginal Effects with 95% CIs") xtitle("") ytitle("Effect on support for child benefits") 

  Variables that uniquely identify margins: universal

. graph save fig3a, replace
(file fig3a.gph saved)

. graph export fig3a.pdf, replace
(file /Users/jordimunoz/Dropbox/immigration and welfare/PSRM/Analysis/Replication/fig3a.pdf written in PDF format)

. 
. *Figure 3b
. reg dv c.prej##universal if immi==1

      Source |       SS           df       MS      Number of obs   =     1.598
-------------+----------------------------------   F(3, 1594)      =    158,15
       Model |  281,366646         3  93,7888821   Prob > F        =    0,0000
    Residual |  945,279787     1.594  ,593023706   R-squared       =    0,2294
-------------+----------------------------------   Adj R-squared   =    0,2279
       Total |  1226,64643     1.597  ,768094197   Root MSE        =    ,77008

---------------------------------------------------------------------------------------
                   dv |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
            prejudice |  -,1389694   ,0087359   -15,91   0,000    -,1561044   -,1218344
                      |
            universal |
           Universal  |  -,1055068    ,082386    -1,28   0,201     -,267103    ,0560894
                      |
universal#c.prejudice |
           Universal  |   ,0072525   ,0124662     0,58   0,561    -,0171993    ,0317042
                      |
                _cons |   2,961338   ,0579797    51,08   0,000     2,847613    3,075062
---------------------------------------------------------------------------------------

. est store Prejudice 

. margins, dydx(prej) at(universal=(0 1))

Average marginal effects                        Number of obs     =      1.598
Model VCE    : OLS

Expression   : Linear prediction, predict()
dy/dx w.r.t. : prejudice

1._at        : universal       =           0

2._at        : universal       =           1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
prejudice    |
         _at |
          1  |  -,1389694   ,0087359   -15,91   0,000    -,1561044   -,1218344
          2  |  -,1317169   ,0088932   -14,81   0,000    -,1491606   -,1142733
------------------------------------------------------------------------------

. marginsplot, recast(scatter) recastci(rspike) title("Effects of Cultural prejudice, under immigration treatment") ///
>         subtitle("Average Marginal Effects with 95% CIs") xtitle("") ytitle("Effect on support for child benefits") 

  Variables that uniquely identify margins: universal

. graph save fig3b, replace
(file fig3b.gph saved)

. graph export fig3b.pdf, replace
(file /Users/jordimunoz/Dropbox/immigration and welfare/PSRM/Analysis/Replication/fig3b.pdf written in PDF format)

. 
. *Figure 4a
. reg dv immi##c.education##univ

      Source |       SS           df       MS      Number of obs   =     2.096
-------------+----------------------------------   F(7, 2088)      =     24,64
       Model |  117,374272         7  16,7677532   Prob > F        =    0,0000
    Residual |  1420,68489     2.088   ,68040464   R-squared       =    0,0763
-------------+----------------------------------   Adj R-squared   =    0,0732
       Total |  1538,05916     2.095  ,734157117   Root MSE        =    ,82487

------------------------------------------------------------------------------------------------
                            dv |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------------+----------------------------------------------------------------
                          immi |
          Immigration priming  |  -1,058502   ,1580312    -6,70   0,000    -1,368417   -,7485866
                    education1 |   ,0252324   ,0350233     0,72   0,471    -,0434519    ,0939166
                               |
             immi#c.education1 |
          Immigration priming  |   ,1918815   ,0487355     3,94   0,000     ,0963063    ,2874567
                               |
                     universal |
                    Universal  |  -,1719738    ,159399    -1,08   0,281    -,4845713    ,1406238
                               |
                immi#universal |
Immigration priming#Universal  |   ,4244383   ,2240142     1,89   0,058     -,014876    ,8637527
                               |
        universal#c.education1 |
                    Universal  |   -,020808   ,0492403    -0,42   0,673    -,1173731    ,0757572
                               |
   immi#universal#c.education1 |
Immigration priming#Universal  |  -,0584861   ,0691335    -0,85   0,398    -,1940638    ,0770916
                               |
                         _cons |    2,57989   ,1131879    22,79   0,000     2,357917    2,801863
------------------------------------------------------------------------------------------------

. est store Education

. margins, over(immi) at(education=(1 2 3 4) univ=(0 1) )

Predictive margins                              Number of obs     =      2.096
Model VCE    : OLS

Expression   : Linear prediction, predict()
over         : immi

1._at        : 0.immi
                   education1      =           1
                   universal       =           0
               1.immi
                   education1      =           1
                   universal       =           0

2._at        : 0.immi
                   education1      =           1
                   universal       =           1
               1.immi
                   education1      =           1
                   universal       =           1

3._at        : 0.immi
                   education1      =           2
                   universal       =           0
               1.immi
                   education1      =           2
                   universal       =           0

4._at        : 0.immi
                   education1      =           2
                   universal       =           1
               1.immi
                   education1      =           2
                   universal       =           1

5._at        : 0.immi
                   education1      =           3
                   universal       =           0
               1.immi
                   education1      =           3
                   universal       =           0

6._at        : 0.immi
                   education1      =           3
                   universal       =           1
               1.immi
                   education1      =           3
                   universal       =           1

7._at        : 0.immi
                   education1      =           4
                   universal       =           0
               1.immi
                   education1      =           4
                   universal       =           0

8._at        : 0.immi
                   education1      =           4
                   universal       =           1
               1.immi
                   education1      =           4
                   universal       =           1

-------------------------------------------------------------------------------------------
                          |            Delta-method
                          |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------------+----------------------------------------------------------------
                 _at#immi |
1#No immigration priming  |   2,605123   ,0807257    32,27   0,000     2,446811    2,763434
   1#Immigration priming  |   1,738502   ,0789492    22,02   0,000     1,583675     1,89333
2#No immigration priming  |   2,412341   ,0803385    30,03   0,000     2,254789    2,569893
   2#Immigration priming  |   1,911673   ,0802007    23,84   0,000     1,754391    2,068954
3#No immigration priming  |   2,630355   ,0517207    50,86   0,000     2,528925    2,731784
   3#Immigration priming  |   1,955616   ,0509962    38,35   0,000     1,855608    2,055625
4#No immigration priming  |   2,416765   ,0520379    46,44   0,000     2,314714    2,518817
   4#Immigration priming  |   2,049493   ,0516238    39,70   0,000     1,948253    2,150732
5#No immigration priming  |   2,655587   ,0358705    74,03   0,000     2,585242    2,725933
   5#Immigration priming  |    2,17273   ,0355707    61,08   0,000     2,102972    2,242488
6#No immigration priming  |    2,42119   ,0368448    65,71   0,000     2,348933    2,493446
   6#Immigration priming  |   2,187312    ,036201    60,42   0,000     2,116319    2,258306
7#No immigration priming  |    2,68082   ,0484935    55,28   0,000     2,585719     2,77592
   7#Immigration priming  |   2,389844   ,0471908    50,64   0,000     2,297298     2,48239
8#No immigration priming  |   2,425614   ,0490213    49,48   0,000     2,329478     2,52175
   8#Immigration priming  |   2,325132   ,0486687    47,77   0,000     2,229688    2,420576
-------------------------------------------------------------------------------------------

. marginsplot, by(universal) recastci(rspike) byopt(title("Support for child benefits, by treatment and education")) xtitle(
> "") ytitle("Predicted Support")

  Variables that uniquely identify margins: education1 universal immi

. graph save fig4a, replace
(file fig4a.gph saved)

. graph export fig4a.pdf, replace
(file /Users/jordimunoz/Dropbox/immigration and welfare/PSRM/Analysis/Replication/fig4a.pdf written in PDF format)

. 
. *Figure 4b
. reg dv immi##c.socgrade##univ

      Source |       SS           df       MS      Number of obs   =     3.258
-------------+----------------------------------   F(7, 3250)      =     29,04
       Model |  143,484365         7  20,4977664   Prob > F        =    0,0000
    Residual |   2294,2974     3.250  ,705937662   R-squared       =    0,0589
-------------+----------------------------------   Adj R-squared   =    0,0568
       Total |  2437,78177     3.257  ,748474599   Root MSE        =     ,8402

------------------------------------------------------------------------------------------------
                            dv |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------------+----------------------------------------------------------------
                          immi |
          Immigration priming  |  -,3836142   ,0901186    -4,26   0,000    -,5603092   -,2069192
                      socgrade |  -,0084063   ,0262395    -0,32   0,749     -,059854    ,0430414
                               |
               immi#c.socgrade |
          Immigration priming  |  -,0379983   ,0371157    -1,02   0,306    -,1107707    ,0347742
                               |
                     universal |
                    Universal  |   -,290804   ,0897264    -3,24   0,001      -,46673    -,114878
                               |
                immi#universal |
Immigration priming#Universal  |   ,1748743   ,1270024     1,38   0,169    -,0741386    ,4238871
                               |
          universal#c.socgrade |
                    Universal  |   ,0277422   ,0370656     0,75   0,454    -,0449322    ,1004166
                               |
     immi#universal#c.socgrade |
Immigration priming#Universal  |  -,0015028    ,052615    -0,03   0,977    -,1046647     ,101659
                               |
                         _cons |   2,637164   ,0637152    41,39   0,000     2,512238     2,76209
------------------------------------------------------------------------------------------------

. est store SocialGrade

. margins, over(immi) at(socgrade=(1 2 3 4) univ=(0 1))

Predictive margins                              Number of obs     =      3.258
Model VCE    : OLS

Expression   : Linear prediction, predict()
over         : immi

1._at        : 0.immi
                   socgrade        =           1
                   universal       =           0
               1.immi
                   socgrade        =           1
                   universal       =           0

2._at        : 0.immi
                   socgrade        =           1
                   universal       =           1
               1.immi
                   socgrade        =           1
                   universal       =           1

3._at        : 0.immi
                   socgrade        =           2
                   universal       =           0
               1.immi
                   socgrade        =           2
                   universal       =           0

4._at        : 0.immi
                   socgrade        =           2
                   universal       =           1
               1.immi
                   socgrade        =           2
                   universal       =           1

5._at        : 0.immi
                   socgrade        =           3
                   universal       =           0
               1.immi
                   socgrade        =           3
                   universal       =           0

6._at        : 0.immi
                   socgrade        =           3
                   universal       =           1
               1.immi
                   socgrade        =           3
                   universal       =           1

7._at        : 0.immi
                   socgrade        =           4
                   universal       =           0
               1.immi
                   socgrade        =           4
                   universal       =           0

8._at        : 0.immi
                   socgrade        =           4
                   universal       =           1
               1.immi
                   socgrade        =           4
                   universal       =           1

-------------------------------------------------------------------------------------------
                          |            Delta-method
                          |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------------+----------------------------------------------------------------
                 _at#immi |
1#No immigration priming  |   2,628757   ,0421966    62,30   0,000     2,546023    2,711492
   1#Immigration priming  |   2,207145   ,0421117    52,41   0,000     2,124577    2,289713
2#No immigration priming  |   2,365696   ,0418887    56,48   0,000     2,283565    2,447827
   2#Immigration priming  |   2,117455   ,0418206    50,63   0,000     2,035457    2,199452
3#No immigration priming  |   2,620351   ,0296396    88,41   0,000     2,562237    2,678465
   3#Immigration priming  |    2,16074   ,0293799    73,54   0,000     2,103135    2,218345
4#No immigration priming  |   2,385032   ,0298132    80,00   0,000     2,326577    2,443486
   4#Immigration priming  |   2,097289   ,0298613    70,23   0,000      2,03874    2,155838
5#No immigration priming  |   2,611945   ,0367898    71,00   0,000     2,539811    2,684078
   5#Immigration priming  |   2,114336    ,036484    57,95   0,000     2,042802     2,18587
6#No immigration priming  |   2,404367   ,0373321    64,40   0,000     2,331171    2,477564
   6#Immigration priming  |   2,077124   ,0380166    54,64   0,000     2,002585    2,151663
7#No immigration priming  |   2,603539   ,0566171    45,99   0,000      2,49253    2,714547
   7#Immigration priming  |   2,067931   ,0563657    36,69   0,000     1,957415    2,178447
8#No immigration priming  |   2,423703   ,0571773    42,39   0,000     2,311596     2,53581
   8#Immigration priming  |   2,056959   ,0583922    35,23   0,000      1,94247    2,171448
-------------------------------------------------------------------------------------------

. marginsplot, by(universal) recastci(rspike) byopt(title("Support for child benefits, by treatment and Social Grade")) xtit
> le("") ytitle("Predicted Support")

  Variables that uniquely identify margins: socgrade universal immi

. graph save fig4b, replace
(file fig4b.gph saved)

. graph export fig4b.pdf, replace
(file /Users/jordimunoz/Dropbox/immigration and welfare/PSRM/Analysis/Replication/fig4b.pdf written in PDF format)

. 
. *Figure 5a
. reg dv immi##c.deserving1##c.education 

      Source |       SS           df       MS      Number of obs   =     2.006
-------------+----------------------------------   F(7, 1998)      =     69,81
       Model |  290,652405         7  41,5217722   Prob > F        =    0,0000
    Residual |  1188,34311     1.998   ,59476632   R-squared       =    0,1965
-------------+----------------------------------   Adj R-squared   =    0,1937
       Total |  1478,99551     2.005  ,737653623   Root MSE        =    ,77121

------------------------------------------------------------------------------------------------
                            dv |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------------+----------------------------------------------------------------
                          immi |
          Immigration priming  |  -,2167655   ,2577197    -0,84   0,400     -,722193     ,288662
                    deserving1 |   ,0073457   ,0245975     0,30   0,765    -,0408938    ,0555851
                               |
             immi#c.deserving1 |
          Immigration priming  |  -,0852339   ,0356471    -2,39   0,017    -,1551433   -,0153245
                               |
                    education1 |   ,1761327   ,0543796     3,24   0,001      ,069486    ,2827794
                               |
             immi#c.education1 |
          Immigration priming  |   ,0546946   ,0785219     0,70   0,486    -,0992987    ,2086879
                               |
     c.deserving1#c.education1 |  -,0282589   ,0078551    -3,60   0,000     -,043664   -,0128538
                               |
immi#c.deserving1#c.education1 |
          Immigration priming  |   ,0129522   ,0111472     1,16   0,245    -,0089091    ,0348136
                               |
                         _cons |   2,475754   ,1749788    14,15   0,000     2,132594    2,818913
------------------------------------------------------------------------------------------------

. est store Education_des

. margins, dydx(deserving1) at(education=(1 2 3 4) immi=(0 1))

Average marginal effects                        Number of obs     =      2.006
Model VCE    : OLS

Expression   : Linear prediction, predict()
dy/dx w.r.t. : deserving1

1._at        : immi            =           0
               education1      =           1

2._at        : immi            =           0
               education1      =           2

3._at        : immi            =           0
               education1      =           3

4._at        : immi            =           0
               education1      =           4

5._at        : immi            =           1
               education1      =           1

6._at        : immi            =           1
               education1      =           2

7._at        : immi            =           1
               education1      =           3

8._at        : immi            =           1
               education1      =           4

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
deserving1   |
         _at |
          1  |  -,0209133   ,0174524    -1,20   0,231    -,0551401    ,0133136
          2  |  -,0491722   ,0112935    -4,35   0,000    -,0713204    -,027024
          3  |  -,0774311   ,0085968    -9,01   0,000    -,0942907   -,0605715
          4  |    -,10569   ,0119864    -8,82   0,000    -,1291971   -,0821829
          5  |   -,093195   ,0184741    -5,04   0,000    -,1294254   -,0569645
          6  |  -,1085016   ,0119171    -9,10   0,000    -,1318729   -,0851304
          7  |  -,1238083   ,0082374   -15,03   0,000    -,1399632   -,1076535
          8  |   -,139115   ,0108998   -12,76   0,000    -,1604913   -,1177388
------------------------------------------------------------------------------

. marginsplot, recastci(rspike) title("Effect of deservingness beliefs, by treatment and Education") ytitle("Effects on Supp
> ort for Child Benefits") yline(0)

  Variables that uniquely identify margins: education1 immi

. graph save fig5a, replace
(file fig5a.gph saved)

. graph export fig5a.pdf, replace
(file /Users/jordimunoz/Dropbox/immigration and welfare/PSRM/Analysis/Replication/fig5a.pdf written in PDF format)

. 
. *Figure 5b
. reg dv immi##c.deserving1##c.socgrade

      Source |       SS           df       MS      Number of obs   =     3.121
-------------+----------------------------------   F(7, 3113)      =     92,98
       Model |  405,426951         7  57,9181359   Prob > F        =    0,0000
    Residual |  1939,04405     3.113  ,622885979   R-squared       =    0,1729
-------------+----------------------------------   Adj R-squared   =    0,1711
       Total |    2344,471     3.120  ,751433014   Root MSE        =    ,78923

----------------------------------------------------------------------------------------------
                          dv |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------------+----------------------------------------------------------------
                        immi |
        Immigration priming  |  -,0862075   ,1496115    -0,58   0,565    -,3795548    ,2071398
                  deserving1 |  -,1064262   ,0153482    -6,93   0,000    -,1365199   -,0763325
                             |
           immi#c.deserving1 |
        Immigration priming  |  -,0313393   ,0215853    -1,45   0,147    -,0736623    ,0109836
                             |
                    socgrade |  -,0855948   ,0402002    -2,13   0,033    -,1644164   -,0067733
                             |
             immi#c.socgrade |
        Immigration priming  |   ,0058716   ,0576668     0,10   0,919    -,1071972    ,1189404
                             |
     c.deserving1#c.socgrade |   ,0126693   ,0058946     2,15   0,032     ,0011117    ,0242269
                             |
immi#c.deserving1#c.socgrade |
        Immigration priming  |  -,0070975   ,0083728    -0,85   0,397    -,0235144    ,0093193
                             |
                       _cons |   3,178405    ,105422    30,15   0,000     2,971701    3,385109
----------------------------------------------------------------------------------------------

. est store SocialGrade_des 

. margins, dydx(deserving1) at(socgrade=(1 2 3 4) immi=(0 1))

Average marginal effects                        Number of obs     =      3.121
Model VCE    : OLS

Expression   : Linear prediction, predict()
dy/dx w.r.t. : deserving1

1._at        : immi            =           0
               socgrade        =           1

2._at        : immi            =           0
               socgrade        =           2

3._at        : immi            =           0
               socgrade        =           3

4._at        : immi            =           0
               socgrade        =           4

5._at        : immi            =           1
               socgrade        =           1

6._at        : immi            =           1
               socgrade        =           2

7._at        : immi            =           1
               socgrade        =           3

8._at        : immi            =           1
               socgrade        =           4

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
deserving1   |
         _at |
          1  |  -,0937569   ,0104453    -8,98   0,000    -,1142372   -,0732766
          2  |  -,0810876   ,0072201   -11,23   0,000    -,0952443   -,0669309
          3  |  -,0684183   ,0080404    -8,51   0,000    -,0841835   -,0526532
          4  |  -,0557491   ,0121103    -4,60   0,000     -,079494   -,0320041
          5  |  -,1321938   ,0102008   -12,96   0,000    -,1521947   -,1121928
          6  |  -,1266221   ,0069621   -18,19   0,000    -,1402728   -,1129713
          7  |  -,1210503   ,0079751   -15,18   0,000    -,1366874   -,1054133
          8  |  -,1154786    ,012225    -9,45   0,000    -,1394485   -,0915087
------------------------------------------------------------------------------

. marginsplot, recastci(rspike) title("Effect of deservingness beliefs, by treatment and Social Grade") ytitle("Effects on S
> upport for Child Benefits") yline(0)

  Variables that uniquely identify margins: socgrade immi

. graph save fig5b, replace
(file fig5b.gph saved)

. graph export fig5b.pdf, replace
(file /Users/jordimunoz/Dropbox/immigration and welfare/PSRM/Analysis/Replication/fig5b.pdf written in PDF format)

. 
. *Figure 6a
. reg dv immi##c.prej##c.education

      Source |       SS           df       MS      Number of obs   =     2.049
-------------+----------------------------------   F(7, 2041)      =     63,09
       Model |  268,136009         7  38,3051442   Prob > F        =    0,0000
    Residual |  1239,21099     2.041   ,60715874   R-squared       =    0,1779
-------------+----------------------------------   Adj R-squared   =    0,1751
       Total |    1507,347     2.048  ,736009277   Root MSE        =     ,7792

-----------------------------------------------------------------------------------------------
                           dv |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                         immi |
         Immigration priming  |   ,3622135   ,2866127     1,26   0,206    -,1998704    ,9242973
                    prejudice |   ,0037853   ,0259653     0,15   0,884    -,0471358    ,0547065
                              |
             immi#c.prejudice |
         Immigration priming  |   -,135047   ,0378915    -3,56   0,000     -,209357   -,0607369
                              |
                   education1 |   ,0626925   ,0551339     1,14   0,256     -,045432     ,170817
                              |
            immi#c.education1 |
         Immigration priming  |  -,0425353   ,0811728    -0,52   0,600    -,2017254    ,1166548
                              |
     c.prejudice#c.education1 |  -,0118859   ,0079277    -1,50   0,134     -,027433    ,0036613
                              |
immi#c.prejudice#c.education1 |
         Immigration priming  |   ,0101079   ,0114456     0,88   0,377    -,0123384    ,0325543
                              |
                        _cons |   2,515868   ,1915684    13,13   0,000     2,140178    2,891558
-----------------------------------------------------------------------------------------------

. est store Education_prej

. margins, dydx(prej) at(education=(1 2 3 4) immi=(0 1))

Average marginal effects                        Number of obs     =      2.049
Model VCE    : OLS

Expression   : Linear prediction, predict()
dy/dx w.r.t. : prejudice

1._at        : immi            =           0
               education1      =           1

2._at        : immi            =           0
               education1      =           2

3._at        : immi            =           0
               education1      =           3

4._at        : immi            =           0
               education1      =           4

5._at        : immi            =           1
               education1      =           1

6._at        : immi            =           1
               education1      =           2

7._at        : immi            =           1
               education1      =           3

8._at        : immi            =           1
               education1      =           4

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
prejudice    |
         _at |
          1  |  -,0081005   ,0186538    -0,43   0,664     -,044683    ,0284819
          2  |  -,0199864    ,012142    -1,65   0,100    -,0437985    ,0038257
          3  |  -,0318723   ,0085199    -3,74   0,000     -,048581   -,0151636
          4  |  -,0437581   ,0111106    -3,94   0,000    -,0655474   -,0219688
          5  |  -,1330396   ,0199167    -6,68   0,000    -,1720987   -,0939804
          6  |  -,1348175   ,0129648   -10,40   0,000     -,160243    -,109392
          7  |  -,1365954   ,0087066   -15,69   0,000    -,1536702   -,1195207
          8  |  -,1383734   ,0109469   -12,64   0,000    -,1598416   -,1169051
------------------------------------------------------------------------------

. marginsplot, recastci(rspike) ///
>         title("Effect of Prejudice, by treatment and Education") ///
>         subtitle("Average Marginal Effects and 95% CI") yline(0)

  Variables that uniquely identify margins: education1 immi

. graph save fig6a, replace
(file fig6a.gph saved)

. graph export fig6a.pdf, replace
(file /Users/jordimunoz/Dropbox/immigration and welfare/PSRM/Analysis/Replication/fig6a.pdf written in PDF format)

. 
. *Figure 6b
. reg dv immi##c.prej##c.socgrade 

      Source |       SS           df       MS      Number of obs   =     3.180
-------------+----------------------------------   F(7, 3172)      =     98,04
       Model |  424,499206         7  60,6427437   Prob > F        =    0,0000
    Residual |  1961,94859     3.172  ,618520994   R-squared       =    0,1779
-------------+----------------------------------   Adj R-squared   =    0,1761
       Total |   2386,4478     3.179  ,750691349   Root MSE        =    ,78646

---------------------------------------------------------------------------------------------
                         dv |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
                       immi |
       Immigration priming  |  -,0669343   ,1285751    -0,52   0,603     -,319033    ,1851644
                  prejudice |  -,0730079   ,0144262    -5,06   0,000    -,1012935   -,0447223
                            |
           immi#c.prejudice |
       Immigration priming  |  -,0546263   ,0201504    -2,71   0,007    -,0941355   -,0151171
                            |
                   socgrade |  -,0618116   ,0401526    -1,54   0,124    -,1405393    ,0169161
                            |
            immi#c.socgrade |
       Immigration priming  |   ,1128773   ,0566002     1,99   0,046     ,0019006     ,223854
                            |
     c.prejudice#c.socgrade |   ,0138964   ,0058966     2,36   0,018     ,0023349     ,025458
                            |
immi#c.prejudice#c.socgrade |
       Immigration priming  |  -,0181838   ,0083002    -2,19   0,029    -,0344581   -,0019095
                            |
                      _cons |   2,878206   ,0919213    31,31   0,000     2,697975    3,058437
---------------------------------------------------------------------------------------------

. est store SocialGrade_prej

. margins, dydx(prej) at(socgrade=(1 2 3 4) immi=(0 1))

Average marginal effects                        Number of obs     =      3.180
Model VCE    : OLS

Expression   : Linear prediction, predict()
dy/dx w.r.t. : prejudice

1._at        : immi            =           0
               socgrade        =           1

2._at        : immi            =           0
               socgrade        =           2

3._at        : immi            =           0
               socgrade        =           3

4._at        : immi            =           0
               socgrade        =           4

5._at        : immi            =           1
               socgrade        =           1

6._at        : immi            =           1
               socgrade        =           2

7._at        : immi            =           1
               socgrade        =           3

8._at        : immi            =           1
               socgrade        =           4

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
prejudice    |
         _at |
          1  |  -,0591115   ,0095885    -6,16   0,000    -,0779118   -,0403112
          2  |   -,045215   ,0067308    -6,72   0,000    -,0584123   -,0320178
          3  |  -,0313186   ,0082588    -3,79   0,000    -,0475118   -,0151254
          4  |  -,0174222   ,0126749    -1,37   0,169     -,042274    ,0074296
          5  |  -,1319216   ,0092741   -14,22   0,000    -,1501055   -,1137377
          6  |   -,136209    ,006507   -20,93   0,000    -,1489674   -,1234506
          7  |  -,1404964   ,0081805   -17,17   0,000    -,1565359   -,1244568
          8  |  -,1447838   ,0126391   -11,46   0,000    -,1695653   -,1200022
------------------------------------------------------------------------------

. marginsplot, recastci(rspike) ///
>         title("Effect of Prejudice, by treatment and Social Grade") ///
>         subtitle("Average Marginal Effects and 95% CI") yline(0)

  Variables that uniquely identify margins: socgrade immi

. graph save fig6b, replace
(file fig6b.gph saved)

. graph export fig6b.pdf, replace
(file /Users/jordimunoz/Dropbox/immigration and welfare/PSRM/Analysis/Replication/fig6b.pdf written in PDF format)

.         
. 
end of do-file

. do "/var/folders/ct/q1ww4xg51b7_4qynkp39sn100000gn/T//SD11644.000000"

. *------------------------------------------------------------*
. *----------Appendix------------------------------------------*
. *------------------------------------------------------------*
. 
. 
. *Table A1: Effects of deservingness beliefs and prejudice, under immigration treatment by policy condition (3a & 3b)
. esttab Deservingness Prejudice using models_appendix.rtf, cell(b(star fmt (%9.2f)) se(par)) ///
>  stats(N r2, fmt(%9.0g %9.3f) labels("Observations" "R2")) /// 
>         starlevels(* 0.1 ** 0.05 *** 0.01) legend label varlabels(_cons Constant) noomitted nobaselevels ///
>          title("Table A1: Effects of deservingness beliefs and prejudice, under immigration treatment by policy condition 
> (3a & 3b)") mtitles replace
(output written to models_appendix.rtf)

. 
. *Table A2: Support for child benefits, by treatment and education/Social grade (4a&b)
. esttab Education SocialGrade using models_appendix.rtf, cell(b(star fmt (%9.2f)) se(par)) ///
>  stats(N r2, fmt(%9.0g %9.3f) labels("Observations" "R2")) /// 
>         starlevels(* 0.1 ** 0.05 *** 0.01) legend label varlabels(_cons Constant) noomitted nobaselevels ///
>          title("Table A2: Support for child benefits, by treatment and Education/Social grade (Figures 4a&b)") mtitles app
> end
(output written to models_appendix.rtf)

. 
. *Table A3: Effect of Prejudice, by treatment and Education/Social Grade (6a&b)
. esttab Education_des SocialGrade_des Education_prej SocialGrade_prej using models_appendix.rtf, cell(b(star fmt (%9.2f)) s
> e(par)) ///
>  stats(N r2, fmt(%9.0g %9.3f) labels("Observations" "R2")) /// 
>         starlevels(* 0.1 ** 0.05 *** 0.01) legend label varlabels(_cons Constant) noomitted nobaselevels ///
>          title("Table A3: Effect of Other-regarding considerations, by treatment and Education/Social Grade (Figures 4&5)"
> ) mtitles append
(output written to models_appendix.rtf)

. 
. *Appendix2: ologit
. 
. *Table A2.2.1: Effects of deservingness beliefs and Prejudice, under immigration treatment by policy condition 
. olog dv c.deserving1##universal if immi==1

Iteration 0:   log likelihood =  -1892,409  
Iteration 1:   log likelihood = -1737,1388  
Iteration 2:   log likelihood = -1735,0679  
Iteration 3:   log likelihood = -1735,0628  
Iteration 4:   log likelihood = -1735,0628  

Ordered logistic regression                     Number of obs     =      1.572
                                                LR chi2(3)        =     314,69
                                                Prob > chi2       =     0,0000
Log likelihood = -1735,0628                     Pseudo R2         =     0,0831

----------------------------------------------------------------------------------------
                    dv |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
            deserving1 |   -,344784   ,0254831   -13,53   0,000    -,3947299    -,294838
                       |
             universal |
            Universal  |  -,6151156   ,2452657    -2,51   0,012    -1,095827   -,1344038
                       |
universal#c.deserving1 |
            Universal  |   ,0795852   ,0343564     2,32   0,021      ,012248    ,1469225
-----------------------+----------------------------------------------------------------
                 /cut1 |  -3,266083   ,1917002                     -3,641809   -2,890358
                 /cut2 |  -1,760858   ,1795269                     -2,112724   -1,408992
                 /cut3 |   1,539586   ,1997833                      1,148018    1,931154
----------------------------------------------------------------------------------------

. est store Deservingness_olog

. 
. olog dv c.prej##universal if immi==1

Iteration 0:   log likelihood = -1918,8831  
Iteration 1:   log likelihood = -1707,1868  
Iteration 2:   log likelihood = -1703,5387  
Iteration 3:   log likelihood = -1703,5347  
Iteration 4:   log likelihood = -1703,5347  

Ordered logistic regression                     Number of obs     =      1.598
                                                LR chi2(3)        =     430,70
                                                Prob > chi2       =     0,0000
Log likelihood = -1703,5347                     Pseudo R2         =     0,1122

---------------------------------------------------------------------------------------
                   dv |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
            prejudice |  -,3665535   ,0246335   -14,88   0,000    -,4148344   -,3182727
                      |
            universal |
           Universal  |  -,4113284   ,2180842    -1,89   0,059    -,8387655    ,0161087
                      |
universal#c.prejudice |
           Universal  |   ,0403395   ,0328446     1,23   0,219    -,0240348    ,1047138
----------------------+----------------------------------------------------------------
                /cut1 |  -3,302124   ,1777649                     -3,650537   -2,953711
                /cut2 |  -1,686095   ,1622212                     -2,004042   -1,368147
                /cut3 |   1,718914   ,1901504                      1,346226    2,091602
---------------------------------------------------------------------------------------

. est store Prejudice_olog 

. 
. esttab Deservingness_olog Prejudice_olog using models_appendix.rtf, cell(b(star fmt (%9.2f)) se(par)) ///
>  stats(N r2_p, fmt(%9.0g %9.3f) labels("Observations" "Pseudo-R2")) /// 
>         starlevels(* 0.1 ** 0.05 *** 0.01) legend label varlabels(_cons Constant) noomitted nobaselevels ///
>          title("Table A2.2.1: Effects of deservingness beliefs and prejudice, under immigration treatment by policy condit
> ion (3a & 3b)") mtitles append
(output written to models_appendix.rtf)

. 
. *Table A2.2: Support for child benefits, by treatment and education/Social grade (4a&b)
. olog dv immi##c.education##univ

Iteration 0:   log likelihood = -2447,2893  
Iteration 1:   log likelihood = -2362,9233  
Iteration 2:   log likelihood =  -2362,313  
Iteration 3:   log likelihood = -2362,3128  
Iteration 4:   log likelihood = -2362,3128  

Ordered logistic regression                     Number of obs     =      2.096
                                                LR chi2(7)        =     169,95
                                                Prob > chi2       =     0,0000
Log likelihood = -2362,3128                     Pseudo R2         =     0,0347

------------------------------------------------------------------------------------------------
                            dv |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------------+----------------------------------------------------------------
                          immi |
          Immigration priming  |  -2,322282   ,3693358    -6,29   0,000    -3,046167   -1,598397
                    education1 |   ,0814526   ,0843537     0,97   0,334    -,0838777    ,2467828
                               |
             immi#c.education1 |
          Immigration priming  |   ,3939879   ,1143229     3,45   0,001     ,1699192    ,6180566
                               |
                     universal |
                    Universal  |  -,3309958   ,3722086    -0,89   0,374    -1,060511    ,3985197
                               |
                immi#universal |
Immigration priming#Universal  |   ,9111796   ,5129772     1,78   0,076    -,0942373    1,916597
                               |
        universal#c.education1 |
                    Universal  |  -,0850238   ,1153332    -0,74   0,461    -,3110728    ,1410251
                               |
   immi#universal#c.education1 |
Immigration priming#Universal  |  -,1008381   ,1589278    -0,63   0,526    -,4123308    ,2106547
-------------------------------+----------------------------------------------------------------
                         /cut1 |  -1,886018   ,2741396                     -2,423321   -1,348714
                         /cut2 |  -,5675878    ,270927                     -1,098595   -,0365805
                         /cut3 |   2,744901   ,2866841                       2,18301    3,306791
------------------------------------------------------------------------------------------------

. est store Education_olog

. 
. olog dv immi##c.socgrade##univ

Iteration 0:   log likelihood = -3835,7991  
Iteration 1:   log likelihood = -3733,3794  
Iteration 2:   log likelihood = -3732,8966  
Iteration 3:   log likelihood = -3732,8964  

Ordered logistic regression                     Number of obs     =      3.258
                                                LR chi2(7)        =     205,81
                                                Prob > chi2       =     0,0000
Log likelihood = -3732,8964                     Pseudo R2         =     0,0268

------------------------------------------------------------------------------------------------
                            dv |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------------+----------------------------------------------------------------
                          immi |
          Immigration priming  |  -,9044732   ,2058115    -4,39   0,000    -1,307856   -,5010901
                      socgrade |  -,0240545   ,0610861    -0,39   0,694    -,1437811    ,0956721
                               |
               immi#c.socgrade |
          Immigration priming  |  -,0821672   ,0842066    -0,98   0,329     -,247209    ,0828746
                               |
                     universal |
                    Universal  |  -,6891278   ,2049288    -3,36   0,001    -1,090781   -,2874748
                               |
                immi#universal |
Immigration priming#Universal  |   ,4418229   ,2832946     1,56   0,119    -,1134244    ,9970701
                               |
          universal#c.socgrade |
                    Universal  |   ,0586463    ,084091     0,70   0,486    -,1061691    ,2234616
                               |
     immi#universal#c.socgrade |
Immigration priming#Universal  |  -,0009657   ,1170964    -0,01   0,993    -,2304705    ,2285391
-------------------------------+----------------------------------------------------------------
                         /cut1 |  -2,056853   ,1538839                      -2,35846   -1,755246
                         /cut2 |  -,7659875   ,1503372                     -1,060643   -,4713319
                         /cut3 |   2,521022   ,1665378                      2,194614     2,84743
------------------------------------------------------------------------------------------------

. est store SocialGrade_olog

. 
. esttab Education_olog SocialGrade_olog using models_appendix.rtf, cell(b(star fmt (%9.2f)) se(par)) ///
>  stats(N r2_p, fmt(%9.0g %9.3f) labels("Observations" "Pseudo-R2")) /// 
>         starlevels(* 0.1 ** 0.05 *** 0.01) legend label varlabels(_cons Constant) noomitted nobaselevels ///
>          title("Table A2.2: Support for child benefits, by treatment and Education/Social grade (Figures 4a&b)") mtitles a
> ppend
(output written to models_appendix.rtf)

. 
. *Table A2.3: Effect of Prejudice, by treatment and Education/Social Grade (6a&b)
. 
. olog dv immi##c.deserving1##c.education 

Iteration 0:   log likelihood =  -2346,295  
Iteration 1:   log likelihood = -2127,0485  
Iteration 2:   log likelihood = -2122,3893  
Iteration 3:   log likelihood = -2122,3737  
Iteration 4:   log likelihood = -2122,3737  

Ordered logistic regression                     Number of obs     =      2.006
                                                LR chi2(7)        =     447,84
                                                Prob > chi2       =     0,0000
Log likelihood = -2122,3737                     Pseudo R2         =     0,0954

------------------------------------------------------------------------------------------------
                            dv |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------------+----------------------------------------------------------------
                          immi |
          Immigration priming  |   -,695006   ,6687104    -1,04   0,299    -2,005654    ,6156423
                    deserving1 |   ,0276894   ,0638298     0,43   0,664    -,0974147    ,1527934
                               |
             immi#c.deserving1 |
          Immigration priming  |  -,1771035    ,091504    -1,94   0,053     -,356448     ,002241
                               |
                    education1 |   ,5079484   ,1457075     3,49   0,000     ,2223669    ,7935298
                               |
             immi#c.education1 |
          Immigration priming  |   ,1812687   ,2061299     0,88   0,379    -,2227385    ,5852759
                               |
     c.deserving1#c.education1 |  -,0793059   ,0205844    -3,85   0,000    -,1196506   -,0389611
                               |
immi#c.deserving1#c.education1 |
          Immigration priming  |   ,0238857   ,0287687     0,83   0,406    -,0324998    ,0802713
-------------------------------+----------------------------------------------------------------
                         /cut1 |  -1,749631   ,4650198                     -2,661053   -,8382089
                         /cut2 |  -,2913065   ,4625567                     -1,197901     ,615288
                         /cut3 |   3,323375   ,4784441                      2,385642    4,261108
------------------------------------------------------------------------------------------------

. est store Education_des_olog

. 
. olog dv immi##c.deserving1##c.socgrade

Iteration 0:   log likelihood = -3680,9299  
Iteration 1:   log likelihood = -3383,0534  
Iteration 2:   log likelihood = -3378,8096  
Iteration 3:   log likelihood = -3378,7951  
Iteration 4:   log likelihood = -3378,7951  

Ordered logistic regression                     Number of obs     =      3.121
                                                LR chi2(7)        =     604,27
                                                Prob > chi2       =     0,0000
Log likelihood = -3378,7951                     Pseudo R2         =     0,0821

----------------------------------------------------------------------------------------------
                          dv |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------------+----------------------------------------------------------------
                        immi |
        Immigration priming  |  -,4076121   ,3907264    -1,04   0,297    -1,173422    ,3581976
                  deserving1 |  -,2992415   ,0395961    -7,56   0,000    -,3768485   -,2216346
                             |
           immi#c.deserving1 |
        Immigration priming  |  -,0471845   ,0550657    -0,86   0,392    -,1551112    ,0607423
                             |
                    socgrade |  -,2966143   ,1049818    -2,83   0,005    -,5023749   -,0908538
                             |
             immi#c.socgrade |
        Immigration priming  |   ,0802516   ,1500436     0,53   0,593    -,2138285    ,3743318
                             |
     c.deserving1#c.socgrade |   ,0414199   ,0149648     2,77   0,006     ,0120895    ,0707503
                             |
immi#c.deserving1#c.socgrade |
        Immigration priming  |  -,0237146   ,0213157    -1,11   0,266    -,0654926    ,0180635
-----------------------------+----------------------------------------------------------------
                       /cut1 |  -3,821842   ,2867833                     -4,383927   -3,259757
                       /cut2 |  -2,392459   ,2823754                     -2,945904   -1,839013
                       /cut3 |   1,112657   ,2809722                       ,561962    1,663353
----------------------------------------------------------------------------------------------

. est store SocialGrade_des_olog 

. 
. olog dv immi##c.prej##c.education

Iteration 0:   log likelihood = -2396,2493  
Iteration 1:   log likelihood = -2197,9581  
Iteration 2:   log likelihood = -2194,9998  
Iteration 3:   log likelihood =  -2194,993  
Iteration 4:   log likelihood =  -2194,993  

Ordered logistic regression                     Number of obs     =      2.049
                                                LR chi2(7)        =     402,51
                                                Prob > chi2       =     0,0000
Log likelihood =  -2194,993                     Pseudo R2         =     0,0840

-----------------------------------------------------------------------------------------------
                           dv |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
                         immi |
         Immigration priming  |   1,011389   ,7352213     1,38   0,169    -,4296179    2,452397
                    prejudice |   ,0255995   ,0658617     0,39   0,698    -,1034872    ,1546861
                              |
             immi#c.prejudice |
         Immigration priming  |  -,3374631   ,0974992    -3,46   0,001     -,528558   -,1463681
                              |
                   education1 |   ,1983576   ,1413029     1,40   0,160     -,078591    ,4753063
                              |
            immi#c.education1 |
         Immigration priming  |   -,129488   ,2096492    -0,62   0,537    -,5403929     ,281417
                              |
     c.prejudice#c.education1 |   -,036514   ,0202413    -1,80   0,071    -,0761862    ,0031582
                              |
immi#c.prejudice#c.education1 |
         Immigration priming  |   ,0270796   ,0294753     0,92   0,358    -,0306909    ,0848501
------------------------------+----------------------------------------------------------------
                        /cut1 |  -1,805414   ,4888218                     -2,763487   -,8473413
                        /cut2 |  -,3452051   ,4865794                     -1,298883    ,6084731
                        /cut3 |   3,107516   ,4979936                      2,131466    4,083565
-----------------------------------------------------------------------------------------------

. est store Education_prej_olog

. 
. olog dv immi##c.prej##c.socgrade 

Iteration 0:   log likelihood = -3753,3834  
Iteration 1:   log likelihood = -3443,8898  
Iteration 2:   log likelihood = -3439,9902  
Iteration 3:   log likelihood = -3439,9806  
Iteration 4:   log likelihood = -3439,9806  

Ordered logistic regression                     Number of obs     =      3.180
                                                LR chi2(7)        =     626,81
                                                Prob > chi2       =     0,0000
Log likelihood = -3439,9806                     Pseudo R2         =     0,0835

---------------------------------------------------------------------------------------------
                         dv |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
                       immi |
       Immigration priming  |  -,2631596   ,3296097    -0,80   0,425    -,9091827    ,3828636
                  prejudice |  -,1880837   ,0367909    -5,11   0,000    -,2601925   -,1159748
                            |
           immi#c.prejudice |
       Immigration priming  |  -,1152811   ,0512415    -2,25   0,024    -,2157125   -,0148496
                            |
                   socgrade |  -,1727774   ,1026837    -1,68   0,092    -,3740337     ,028479
                            |
            immi#c.socgrade |
       Immigration priming  |   ,3484004   ,1458411     2,39   0,017      ,062557    ,6342438
                            |
     c.prejudice#c.socgrade |   ,0365085   ,0149529     2,44   0,015     ,0072013    ,0658158
                            |
immi#c.prejudice#c.socgrade |
       Immigration priming  |  -,0525352   ,0212138    -2,48   0,013    -,0941135   -,0109569
----------------------------+----------------------------------------------------------------
                      /cut1 |  -2,856047   ,2423218                     -3,330989   -2,381105
                      /cut2 |  -1,397513   ,2379494                     -1,863885   -,9311404
                      /cut3 |   2,021155   ,2461223                      1,538764    2,503546
---------------------------------------------------------------------------------------------

. est store SocialGrade_prej_olog

.         
. esttab Education_des_olog SocialGrade_des_olog Education_prej_olog SocialGrade_prej_olog using models_appendix.rtf, cell(b
> (star fmt (%9.2f)) se(par)) ///
>  stats(N r2_p, fmt(%9.0g %9.3f) labels("Observations" "Pseudo-R2")) /// 
>         starlevels(* 0.1 ** 0.05 *** 0.01) legend label varlabels(_cons Constant) noomitted nobaselevels ///
>          title("Table A2.3: Effect of Other-reggarding considerations, by treatment and Education/Social Grade (Figures 4&
> 5)") mtitles append
(output written to models_appendix.rtf)

. 
. log close
      name:  <unnamed>
       log:  /Users/jordimunoz/Dropbox/immigration and welfare/PSRM/Analysis/Replication/replication_munoz_pardos_PSRM.log
  log type:  text
 closed on:   4 Apr 2017, 01:17:07
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